Cooperative Tracking Control of VTOL Drones: A Fixed-Time Super-Twisting Approach.

Area of Science:

• Robotics and Control Systems
• Networked Autonomous Systems
• Aerospace Engineering

Background:

• Cooperative tracking of multiple VTOL drones is complex due to nonlinear dynamics, disturbances, and limited bandwidth.
• Existing control methods often fail to address all challenges or provide guaranteed fixed-time convergence.
• Control chattering on sliding manifolds further complicates drone control.

Purpose of the Study:

• To develop a novel adaptive neural network event-triggered fixed-time super-twisting (ANEFS) control strategy for cooperative drone tracking.
• To enhance tracking accuracy and convergence speed while minimizing network communication.
• To ensure robustness against system nonlinearities and uncertainties.

Main Methods:

• A double closed-loop hierarchical control framework is employed.
• An auxiliary variable-based distributed fixed-time estimator (ADFE) is designed for accurate leader trajectory estimation.
• An event-triggered fixed-time super-twisting (EFST) control law and an adaptive neural network fixed-time super-twisting (ANFST) torque controller are implemented.

Main Results:

• The proposed ANEFS strategy achieves fast and accurate cooperative position and attitude tracking for VTOL drones.
• The event-triggered mechanism significantly reduces network communication bandwidth usage.
• The control strategy demonstrates robustness against system nonlinearities and external disturbances.

Conclusions:

• The developed ANEFS control method offers a fast, robust, and resource-efficient solution for cooperative drone tracking in networked environments.
• The study validates the effectiveness through numerical simulations and real-world flight experiments with Crazyflie quadcopters.
• This work advances the capabilities of multi-drone systems in complex operational scenarios.